In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
from datetime import datetime 
In [2]:
covid_df =pd.read_csv("C:/Users/Vinod/Downloads/Covid India Data Analysis Project_files/covid india data sets/covid_19_india.csv")
In [3]:
covid_df.head(10)
Out[3]:
Sno Date Time State/UnionTerritory ConfirmedIndianNational ConfirmedForeignNational Cured Deaths Confirmed
0 1 5/19/2021 6:00:00 PM Kerala 1.0 0.0 0 0 1
1 2 5/19/2021 6:00:00 PM Kerala 1.0 0.0 0 0 1
2 3 5/19/2021 6:00:00 PM Kerala 2.0 0.0 0 0 2
3 4 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
4 5 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
5 6 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
6 7 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
7 8 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
8 9 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
9 10 5/19/2021 6:00:00 PM Kerala 3.0 0.0 0 0 3
In [4]:
covid_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15086 entries, 0 to 15085
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Sno                       15086 non-null  int64  
 1   Date                      15086 non-null  object 
 2   Time                      15086 non-null  object 
 3   State/UnionTerritory      15086 non-null  object 
 4   ConfirmedIndianNational   446 non-null    float64
 5   ConfirmedForeignNational  446 non-null    float64
 6   Cured                     15086 non-null  int64  
 7   Deaths                    15086 non-null  int64  
 8   Confirmed                 15086 non-null  int64  
dtypes: float64(2), int64(4), object(3)
memory usage: 1.0+ MB
In [5]:
covid_df.describe()
Out[5]:
Sno ConfirmedIndianNational ConfirmedForeignNational Cured Deaths Confirmed
count 15086.000000 446.000000 446.000000 1.508600e+04 15086.000000 1.508600e+04
mean 7543.500000 12.188341 1.495516 1.747937e+05 2721.084449 1.942820e+05
std 4355.097416 21.582253 3.576292 3.648330e+05 7182.672358 4.095184e+05
min 1.000000 0.000000 0.000000 0.000000e+00 0.000000 0.000000e+00
25% 3772.250000 1.000000 0.000000 1.685000e+03 12.000000 2.935500e+03
50% 7543.500000 3.000000 0.000000 1.964700e+04 364.000000 2.608150e+04
75% 11314.750000 13.000000 1.000000 2.087552e+05 2170.000000 2.216012e+05
max 15086.000000 177.000000 14.000000 4.927480e+06 83777.000000 5.433506e+06
In [6]:
vaccine_df = pd.read_csv("C:/Users/Vinod/Downloads/Covid India Data Analysis Project_files/covid india data sets/covid_vaccine_statewise.csv")
In [7]:
vaccine_df.head(7)
Out[7]:
Updated On State Total Doses Administered Sessions Sites First Dose Administered Second Dose Administered Male (Doses Administered) Female (Doses Administered) Transgender (Doses Administered) ... 18-44 Years (Doses Administered) 45-60 Years (Doses Administered) 60+ Years (Doses Administered) 18-44 Years(Individuals Vaccinated) 45-60 Years(Individuals Vaccinated) 60+ Years(Individuals Vaccinated) Male(Individuals Vaccinated) Female(Individuals Vaccinated) Transgender(Individuals Vaccinated) Total Individuals Vaccinated
0 16/01/2021 India 48276.0 3455.0 2957.0 48276.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 23757.0 24517.0 2.0 48276.0
1 17/01/2021 India 58604.0 8532.0 4954.0 58604.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 27348.0 31252.0 4.0 58604.0
2 18/01/2021 India 99449.0 13611.0 6583.0 99449.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 41361.0 58083.0 5.0 99449.0
3 19/01/2021 India 195525.0 17855.0 7951.0 195525.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 81901.0 113613.0 11.0 195525.0
4 20/01/2021 India 251280.0 25472.0 10504.0 251280.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 98111.0 153145.0 24.0 251280.0
5 21/01/2021 India 365965.0 32226.0 12600.0 365965.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 132784.0 233143.0 38.0 365965.0
6 22/01/2021 India 549381.0 36988.0 14115.0 549381.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 193899.0 355402.0 80.0 549381.0

7 rows × 24 columns

In [8]:
covid_df.drop(["Sno", "Time", "ConfirmedIndianNational", "ConfirmedForeignNational"], inplace = True, axis = 1)
In [9]:
covid_df.head()
Out[9]:
Date State/UnionTerritory Cured Deaths Confirmed
0 5/19/2021 Kerala 0 0 1
1 5/19/2021 Kerala 0 0 1
2 5/19/2021 Kerala 0 0 2
3 5/19/2021 Kerala 0 0 3
4 5/19/2021 Kerala 0 0 3
In [10]:
covid_df['Date'] = pd.to_datetime(covid_df['Date'], format = '%m/%d/%Y')
In [11]:
covid_df.head()
Out[11]:
Date State/UnionTerritory Cured Deaths Confirmed
0 2021-05-19 Kerala 0 0 1
1 2021-05-19 Kerala 0 0 1
2 2021-05-19 Kerala 0 0 2
3 2021-05-19 Kerala 0 0 3
4 2021-05-19 Kerala 0 0 3
In [12]:
# Active Cases

covid_df['Active_Cases'] = covid_df['Confirmed'] - (covid_df['Cured'] + covid_df['Deaths'])
covid_df.tail()
Out[12]:
Date State/UnionTerritory Cured Deaths Confirmed Active_Cases
15081 2020-02-03 Telangana 485644 3012 536766 48110
15082 2020-02-02 Tripura 36402 450 42776 5924
15083 2020-02-01 Uttarakhand 214426 5132 295790 76232
15084 2020-01-31 Uttar Pradesh 1483249 18072 1637663 136342
15085 2020-01-30 West Bengal 1026492 13576 1171861 131793
In [13]:
statewise = pd.pivot_table(covid_df, values = ["Confirmed","Deaths","Cured"],
                          index = "State/UnionTerritory", aggfunc = max)
In [14]:
statewise["Recovery Rate"] = statewise["Cured"]*100/statewise["Confirmed"]
In [15]:
statewise["Mortality Rate"] = statewise["Deaths"]*100/statewise["Confirmed"]
In [16]:
statewise = statewise.sort_values(by = "Confirmed", ascending = False)
In [17]:
statewise.style.background_gradient(cmap = "cubehelix")
Out[17]:
  Confirmed Cured Deaths Recovery Rate Mortality Rate
State/UnionTerritory          
Maharashtra 5433506 4927480 83777 90.686934 1.541859
Karnataka 2272374 1674487 22838 73.688882 1.005028
Kerala 2200706 1846105 6612 83.886944 0.300449
Tamil Nadu 1664350 1403052 18369 84.300297 1.103674
Uttar Pradesh 1637663 1483249 18072 90.571076 1.103524
Andhra Pradesh 1475372 1254291 9580 85.015237 0.649328
Delhi 1402873 1329899 22111 94.798246 1.576123
West Bengal 1171861 1026492 13576 87.595030 1.158499
Chhattisgarh 925531 823113 12036 88.934136 1.300443
Rajasthan 879664 713129 7080 81.068340 0.804853
Gujarat 766201 660489 9269 86.203098 1.209735
Madhya Pradesh 742718 652612 7139 87.868074 0.961199
Haryana 709689 626852 6923 88.327704 0.975498
Bihar 664115 595377 4039 89.649684 0.608178
Odisha 633302 536595 2357 84.729718 0.372176
Telangana 536766 485644 3012 90.475924 0.561138
Punjab 511652 427058 12317 83.466497 2.407300
Telengana 443360 362160 2312 81.685312 0.521472
Assam 340858 290774 2344 85.306491 0.687676
Jharkhand 320934 284805 4601 88.742545 1.433628
Uttarakhand 295790 214426 5132 72.492647 1.735015
Jammu and Kashmir 251919 197701 3293 78.478003 1.307166
Himachal Pradesh 166678 129330 2460 77.592724 1.475900
Goa 138776 112633 2197 81.161728 1.583127
Puducherry 87749 69060 1212 78.701752 1.381212
Chandigarh 56513 48831 647 86.406667 1.144869
Tripura 42776 36402 450 85.099121 1.051992
Manipur 40683 33466 612 82.260404 1.504314
Meghalaya 24872 19185 355 77.134931 1.427308
Arunachal Pradesh 22462 19977 88 88.936871 0.391773
Nagaland 18714 14079 228 75.232446 1.218339
Ladakh 16784 15031 170 89.555529 1.012869
Sikkim 11689 8427 212 72.093421 1.813671
Dadra and Nagar Haveli and Daman and Diu 9652 8944 4 92.664733 0.041442
Cases being reassigned to states 9265 0 0 0.000000 0.000000
Mizoram 9252 7094 29 76.675313 0.313446
Andaman and Nicobar Islands 6674 6359 92 95.280192 1.378484
Lakshadweep 5212 3915 15 75.115119 0.287797
Unassigned 77 0 0 0.000000 0.000000
Daman & Diu 2 0 0 0.000000 0.000000
In [18]:
# Top 10 Active Cases

top_10_Active_Cases = covid_df.groupby(by = "State/UnionTerritory").max()[['Active_Cases', 'Date']].sort_values(by = ['Active_Cases'], ascending = False).reset_index()

fig = plt.figure(figsize = (16,9))

plt.title("Top 10 states with most active cases in India", size = 25)

ax = sns.barplot(data = top_10_Active_Cases.iloc[:10], y = "Active_Cases", x = "State/UnionTerritory", linewidth =2, edgecolor = "red")

plt.xlabel('States')
plt.ylabel('Total Active Cases')
plt.show()
In [19]:
# Top 10 States with highest deaths

top_10_deaths = covid_df.groupby(by = "State/UnionTerritory").max()[['Deaths', 'Date']].sort_values(by = ['Deaths'], ascending = False).reset_index()

fig = plt.figure(figsize = (18,5))

plt.title("Top 10 states with most deaths in India", size = 25)

ax = sns.barplot(data = top_10_deaths.iloc[:10], y = "Deaths", x = "State/UnionTerritory", linewidth =2, edgecolor = "black")

plt.xlabel('States')
plt.ylabel('Total Death Cases')
plt.show()
In [20]:
# Growth Trend

fig = plt.figure(figsize = (12,6))

ax = sns.lineplot(data = covid_df[covid_df['State/UnionTerritory'].isin(['Maharashtra','Karnataka','Kerala','Tamil Nadu','Uttar Pradesh'])],x = 'Date', y = 'Active_Cases', hue = 'State/UnionTerritory')

ax.set_title("Top 5 Affected states in India", size = 16)
Out[20]:
Text(0.5, 1.0, 'Top 5 Affected states in India')
In [21]:
vaccine_df.head(10)
Out[21]:
Updated On State Total Doses Administered Sessions Sites First Dose Administered Second Dose Administered Male (Doses Administered) Female (Doses Administered) Transgender (Doses Administered) ... 18-44 Years (Doses Administered) 45-60 Years (Doses Administered) 60+ Years (Doses Administered) 18-44 Years(Individuals Vaccinated) 45-60 Years(Individuals Vaccinated) 60+ Years(Individuals Vaccinated) Male(Individuals Vaccinated) Female(Individuals Vaccinated) Transgender(Individuals Vaccinated) Total Individuals Vaccinated
0 16/01/2021 India 48276.0 3455.0 2957.0 48276.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 23757.0 24517.0 2.0 48276.0
1 17/01/2021 India 58604.0 8532.0 4954.0 58604.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 27348.0 31252.0 4.0 58604.0
2 18/01/2021 India 99449.0 13611.0 6583.0 99449.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 41361.0 58083.0 5.0 99449.0
3 19/01/2021 India 195525.0 17855.0 7951.0 195525.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 81901.0 113613.0 11.0 195525.0
4 20/01/2021 India 251280.0 25472.0 10504.0 251280.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 98111.0 153145.0 24.0 251280.0
5 21/01/2021 India 365965.0 32226.0 12600.0 365965.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 132784.0 233143.0 38.0 365965.0
6 22/01/2021 India 549381.0 36988.0 14115.0 549381.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 193899.0 355402.0 80.0 549381.0
7 23/01/2021 India 759008.0 43076.0 15605.0 759008.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 267856.0 491049.0 103.0 759008.0
8 24/01/2021 India 835058.0 49851.0 18111.0 835058.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 296283.0 538647.0 128.0 835058.0
9 25/01/2021 India 1277104.0 55151.0 19682.0 1277104.0 0.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN 444137.0 832766.0 201.0 1277104.0

10 rows × 24 columns

In [22]:
vaccine_df.rename(columns = {'Updated On' : 'Vaccine_Date'}, inplace = True)
In [23]:
vaccine_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7845 entries, 0 to 7844
Data columns (total 24 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   Vaccine_Date                         7845 non-null   object 
 1   State                                7845 non-null   object 
 2   Total Doses Administered             7621 non-null   float64
 3   Sessions                             7621 non-null   float64
 4    Sites                               7621 non-null   float64
 5   First Dose Administered              7621 non-null   float64
 6   Second Dose Administered             7621 non-null   float64
 7   Male (Doses Administered)            7461 non-null   float64
 8   Female (Doses Administered)          7461 non-null   float64
 9   Transgender (Doses Administered)     7461 non-null   float64
 10   Covaxin (Doses Administered)        7621 non-null   float64
 11  CoviShield (Doses Administered)      7621 non-null   float64
 12  Sputnik V (Doses Administered)       2995 non-null   float64
 13  AEFI                                 5438 non-null   float64
 14  18-44 Years (Doses Administered)     1702 non-null   float64
 15  45-60 Years (Doses Administered)     1702 non-null   float64
 16  60+ Years (Doses Administered)       1702 non-null   float64
 17  18-44 Years(Individuals Vaccinated)  3733 non-null   float64
 18  45-60 Years(Individuals Vaccinated)  3734 non-null   float64
 19  60+ Years(Individuals Vaccinated)    3734 non-null   float64
 20  Male(Individuals Vaccinated)         160 non-null    float64
 21  Female(Individuals Vaccinated)       160 non-null    float64
 22  Transgender(Individuals Vaccinated)  160 non-null    float64
 23  Total Individuals Vaccinated         5919 non-null   float64
dtypes: float64(22), object(2)
memory usage: 1.4+ MB
In [24]:
vaccine_df.isnull().sum()
Out[24]:
Vaccine_Date                              0
State                                     0
Total Doses Administered                224
Sessions                                224
 Sites                                  224
First Dose Administered                 224
Second Dose Administered                224
Male (Doses Administered)               384
Female (Doses Administered)             384
Transgender (Doses Administered)        384
 Covaxin (Doses Administered)           224
CoviShield (Doses Administered)         224
Sputnik V (Doses Administered)         4850
AEFI                                   2407
18-44 Years (Doses Administered)       6143
45-60 Years (Doses Administered)       6143
60+ Years (Doses Administered)         6143
18-44 Years(Individuals Vaccinated)    4112
45-60 Years(Individuals Vaccinated)    4111
60+ Years(Individuals Vaccinated)      4111
Male(Individuals Vaccinated)           7685
Female(Individuals Vaccinated)         7685
Transgender(Individuals Vaccinated)    7685
Total Individuals Vaccinated           1926
dtype: int64
In [25]:
vaccination = vaccine_df.drop(columns = ['Sputnik V (Doses Administered)', 'AEFI', '18-44 Years (Doses Administered)','45-60 Years (Doses Administered)','60+ Years (Doses Administered)'], axis = 1)
In [26]:
vaccination.head()
Out[26]:
Vaccine_Date State Total Doses Administered Sessions Sites First Dose Administered Second Dose Administered Male (Doses Administered) Female (Doses Administered) Transgender (Doses Administered) Covaxin (Doses Administered) CoviShield (Doses Administered) 18-44 Years(Individuals Vaccinated) 45-60 Years(Individuals Vaccinated) 60+ Years(Individuals Vaccinated) Male(Individuals Vaccinated) Female(Individuals Vaccinated) Transgender(Individuals Vaccinated) Total Individuals Vaccinated
0 16/01/2021 India 48276.0 3455.0 2957.0 48276.0 0.0 NaN NaN NaN 579.0 47697.0 NaN NaN NaN 23757.0 24517.0 2.0 48276.0
1 17/01/2021 India 58604.0 8532.0 4954.0 58604.0 0.0 NaN NaN NaN 635.0 57969.0 NaN NaN NaN 27348.0 31252.0 4.0 58604.0
2 18/01/2021 India 99449.0 13611.0 6583.0 99449.0 0.0 NaN NaN NaN 1299.0 98150.0 NaN NaN NaN 41361.0 58083.0 5.0 99449.0
3 19/01/2021 India 195525.0 17855.0 7951.0 195525.0 0.0 NaN NaN NaN 3017.0 192508.0 NaN NaN NaN 81901.0 113613.0 11.0 195525.0
4 20/01/2021 India 251280.0 25472.0 10504.0 251280.0 0.0 NaN NaN NaN 3946.0 247334.0 NaN NaN NaN 98111.0 153145.0 24.0 251280.0
In [27]:
# Male vs Female vaccination

male = vaccination['Male(Individuals Vaccinated)'].sum()
female = vaccination['Female(Individuals Vaccinated)'].sum()
px.pie(names = ['Male', 'Female'], values = [male, female], title = 'Male and Female Vaccination')
In [28]:
# Removing the rows

vaccine = vaccine_df[vaccine_df.State != 'India']
vaccine
Out[28]:
Vaccine_Date State Total Doses Administered Sessions Sites First Dose Administered Second Dose Administered Male (Doses Administered) Female (Doses Administered) Transgender (Doses Administered) ... 18-44 Years (Doses Administered) 45-60 Years (Doses Administered) 60+ Years (Doses Administered) 18-44 Years(Individuals Vaccinated) 45-60 Years(Individuals Vaccinated) 60+ Years(Individuals Vaccinated) Male(Individuals Vaccinated) Female(Individuals Vaccinated) Transgender(Individuals Vaccinated) Total Individuals Vaccinated
212 16/01/2021 Andaman and Nicobar Islands 23.0 2.0 2.0 23.0 0.0 12.0 11.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 23.0
213 17/01/2021 Andaman and Nicobar Islands 23.0 2.0 2.0 23.0 0.0 12.0 11.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 23.0
214 18/01/2021 Andaman and Nicobar Islands 42.0 9.0 2.0 42.0 0.0 29.0 13.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 42.0
215 19/01/2021 Andaman and Nicobar Islands 89.0 12.0 2.0 89.0 0.0 53.0 36.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 89.0
216 20/01/2021 Andaman and Nicobar Islands 124.0 16.0 3.0 124.0 0.0 67.0 57.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 124.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7840 11/08/2021 West Bengal NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7841 12/08/2021 West Bengal NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7842 13/08/2021 West Bengal NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7843 14/08/2021 West Bengal NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7844 15/08/2021 West Bengal NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

7633 rows × 24 columns

In [29]:
vaccine.rename(columns = {'Total Individuals Vaccinated' : 'Total'}, inplace = True)
vaccine.head()
C:\Users\Vinod\AppData\Local\Temp\ipykernel_8940\1867626165.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Out[29]:
Vaccine_Date State Total Doses Administered Sessions Sites First Dose Administered Second Dose Administered Male (Doses Administered) Female (Doses Administered) Transgender (Doses Administered) ... 18-44 Years (Doses Administered) 45-60 Years (Doses Administered) 60+ Years (Doses Administered) 18-44 Years(Individuals Vaccinated) 45-60 Years(Individuals Vaccinated) 60+ Years(Individuals Vaccinated) Male(Individuals Vaccinated) Female(Individuals Vaccinated) Transgender(Individuals Vaccinated) Total
212 16/01/2021 Andaman and Nicobar Islands 23.0 2.0 2.0 23.0 0.0 12.0 11.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 23.0
213 17/01/2021 Andaman and Nicobar Islands 23.0 2.0 2.0 23.0 0.0 12.0 11.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 23.0
214 18/01/2021 Andaman and Nicobar Islands 42.0 9.0 2.0 42.0 0.0 29.0 13.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 42.0
215 19/01/2021 Andaman and Nicobar Islands 89.0 12.0 2.0 89.0 0.0 53.0 36.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 89.0
216 20/01/2021 Andaman and Nicobar Islands 124.0 16.0 3.0 124.0 0.0 67.0 57.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 124.0

5 rows × 24 columns

In [30]:
# Most vaccinated State

max_vac = vaccine.groupby('State')['Total'].sum().to_frame('Total')
max_vac = max_vac.sort_values('Total', ascending = False)[:5]
max_vac
Out[30]:
Total
State
Maharashtra 1.403075e+09
Uttar Pradesh 1.200575e+09
Rajasthan 1.141163e+09
Gujarat 1.078261e+09
West Bengal 9.250227e+08
In [31]:
# Top 5 Vaccinated States in India

fig = plt.figure(figsize = (10,5))

plt.title("Top 5 Vaccinated States in India", size = 20)

x = sns.barplot(data = max_vac.iloc[:10], y = max_vac.Total, x = max_vac.index, linewidth =2, edgecolor = "black")

plt.xlabel('States')
plt.ylabel('Vaccination')
plt.show()
In [32]:
min_vac = vaccine.groupby('State')['Total'].sum().to_frame('Total')
min_vac = min_vac.sort_values('Total', ascending = True)[:5]
min_vac
Out[32]:
Total
State
Lakshadweep 2124715.0
Andaman and Nicobar Islands 8102125.0
Ladakh 9466289.0
Dadra and Nagar Haveli and Daman and Diu 11358600.0
Sikkim 16136752.0
In [33]:
# Least 5 Vaccinated States in India

fig = plt.figure(figsize = (15,5))

plt.title("Least 5 Vaccinated States in India", size = 20)

x = sns.barplot(data = min_vac.iloc[:10], y = min_vac.Total, x = min_vac.index, linewidth =2, edgecolor = "black")

plt.xlabel('States')
plt.ylabel('Vaccination')
plt.show()

CONCLUSION

  • States with most active cases in india are Maharastra, Karnataka, Kerala, Uttar Pradesh and Tamilnadu etc..,
  • States with most death cases in india are Maharastra, Karnataka, Delhi, Tamilnadu and Uttar Pradesh etc..,
  • Most affected states in india are Kerala, Uttar Pradesh, Tamilnadu, Karnataka and Maharastra etc..,
  • Total vaccinated females is 47% and total vaccinated males is 53%.
  • Highest Vaccinated states in india are Maharastra, Uttar pradesh, Rajasthan, Gujarat and West Bengal etc..,
  • Least Vaccinated states in india are Lakshadweep, Andaman and Nicobar islands, Ladakh and Sikkim etc..,